code: def test_predict_feature_columns_pandas():
      pandas_data = pd.DataFrame(dummy_data, columns=["A", "B"])
      pandas_target = pd.Series(dummy_target)
      pandas_model = (
          lgbm.LGBMClassifier(n_estimators=10)
          .fit(pandas_data, pandas_target)
          .booster_
      )
      preprocessor = DummyPreprocessor()
      predictor = LightGBMPredictor(
          model=pandas_model, preprocessor=preprocessor
      )
      data_batch = pd.DataFrame(
          np.array([[1, 2, 7], [3, 4, 8], [5, 6, 9]]),
          columns=["A", "B", "C"],
      )
      predictions = predictor.predict(
          data_batch, feature_columns=["A", "B"]
      )
  
      assert len(predictions) == 3
      assert predictor.get_preprocessor().has_preprocessed
  
file_path: ray/python/ray/train/tests/test_lightgbm_predictor.py
imports: import re
  import lightgbm
  import numpy
  import pandas
  import pytest
  from ray.air.constants import MAX_REPR_LENGTH
  from ray.air.util.data_batch_conversion import _convert_pandas_to_batch_type
  from ray.train.lightgbm import LightGBMCheckpoint, LightGBMPredictor
  from ray.train.predictor import TYPE_TO_ENUM
  from ray.train.tests.dummy_preprocessor import DummyPreprocessor
label_desc: Use image node_id 6 for calling a global function with example usage: test_predict_feature_columns_pandas() without return types